{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/code/find-files","entry":"find_files","source":"Syntology graph, per-sample; not an archive number","read_at":"2026-09-24T18:15:14+00:00","claim":"Names are grouped by exact entry-name string. Same-named routines are NOT asserted to be equivalent; 'ran' means executed on a synthesized fixture, not correctness. n_samples_ran = sum of by_status over every status except 'unverified' (ran_draft_wrong and ran_fixture are failures of Syntology's instrument, not of the code); n_papers_ran = papers with at least one such sample.","status_vocabulary":{"ran_honours":"ran, honoured the contract we drafted","ran_violates":"ran, violated the contract we drafted","ran_draft_wrong":"ran; our contract draft was wrong, not the code","ran_fixture":"ran; our fixture could not drive it","ran":"ran on a synthesized input","unverified":"unverified (harvested, no recorded run)"},"n_papers":21,"n_papers_ran":13,"units":"n_samples, n_samples_ran, n_samples_fingerprinted and by_status count distinct code bodies (code_sha256); n_places and n_places_pointer_only count places, one per (paper, code body) pair, which is also the unit of the samples list","n_samples":16,"n_samples_ran":8,"n_samples_fingerprinted":2,"n_places":21,"n_places_pointer_only":10,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":6,"ran_fixture":0,"ran":2,"unverified":8},"syntology":{"atlas_url":null,"mcp":null,"mcp_per_sample":{"tool":"get_code","arguments_in":"samples[].mcp_get_code"},"developers":"https://syntology.ai/developers"},"samples":[{"arxiv_id":"2506.05982","paper":"/paper/mca-bench-a-multimodal-benchmark-for","title":"MCA-Bench: A Multimodal Benchmark for Evaluating CAPTCHA Robustness Against VLM-based Attacks","date":"2025-06-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"noheadwuzonglin/mca-bench","path":"tojson/clickmathtojson.py","file_url":"https://github.com/noheadwuzonglin/mca-bench/blob/HEAD/tojson/clickmathtojson.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"1aeb97e5409a3096","mcp_get_code":{"code_sha256":"1aeb97e5409a3096"}},{"arxiv_id":"2502.09449","paper":"/paper/spiking-neural-networks-for-temporal","title":"Spiking Neural Networks for Temporal Processing: Status Quo and Future Prospects","date":"2025-02-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"liyc5929/neuroseqbench","path":"experiments/neuromorphic_sequential_arena/ASR/egs2/TEMPLATE/asr1/pyscripts/utils/evaluate_speechbertscore.py","file_url":"https://github.com/liyc5929/neuroseqbench/blob/HEAD/experiments/neuromorphic_sequential_arena/ASR/egs2/TEMPLATE/asr1/pyscripts/utils/evaluate_speechbertscore.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"GPL-3.0","inline_ok":false,"code_sha256_prefix":"c9763c5a4bcb7e32","mcp_get_code":{"code_sha256":"c9763c5a4bcb7e32"}},{"arxiv_id":"2412.18381","paper":"/paper/mr-cographs-communication-efficient-multi","title":"MR-COGraphs: Communication-efficient Multi-Robot Open-vocabulary Mapping System via 3D Scene Graphs","date":null,"month_inferred_from_arxiv_id":"2024-12","title_source":"archive","repo":"efc-robot/mr-cographs","path":"r3d_to_ROS/r3d_to_rosbag.py","file_url":"https://github.com/efc-robot/mr-cographs/blob/HEAD/r3d_to_ROS/r3d_to_rosbag.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"dec992008281259e","mcp_get_code":{"code_sha256":"dec992008281259e"}},{"arxiv_id":"2411.05823","paper":"/paper/flexcad-unified-and-versatile-controllable","title":"FlexCAD: Unified and Versatile Controllable CAD Generation with Fine-tuned Large Language Models","date":"2024-11-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"microsoft/flexcad","path":"utils/eval_cad.py","file_url":"https://github.com/microsoft/flexcad/blob/HEAD/utils/eval_cad.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"680bb1e97d16d80c","mcp_get_code":{"code_sha256":"680bb1e97d16d80c"}},{"arxiv_id":"2410.13184","paper":"/paper/router-tuning-a-simple-and-effective-approach","title":"Router-Tuning: A Simple and Effective Approach for Enabling Dynamic-Depth in Transformers","date":"2024-10-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"case-lab-umd/router-tuning","path":"utils/io.py","file_url":"https://github.com/case-lab-umd/router-tuning/blob/HEAD/utils/io.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"b317237cdf13806f","mcp_get_code":{"code_sha256":"b317237cdf13806f"}},{"arxiv_id":"2408.06072","paper":"/paper/cogvideox-text-to-video-diffusion-models-with","title":"CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer","date":"2024-08-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"thudm/cogvideo","path":"finetune/utils/file_utils.py","file_url":"https://github.com/thudm/cogvideo/blob/HEAD/finetune/utils/file_utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"b79185cd9f8337d1","mcp_get_code":{"code_sha256":"b79185cd9f8337d1"}},{"arxiv_id":"2406.12194","paper":"/paper/universal-score-based-speech-enhancement-with","title":"Universal Score-based Speech Enhancement with High Content Preservation","date":"2024-06-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"65067ffb6e848832","mcp_get_code":{"code_sha256":"65067ffb6e848832"}},{"arxiv_id":"2401.17230","paper":"/paper/espnet-spk-full-pipeline-speaker-embedding","title":"ESPnet-SPK: full pipeline speaker embedding toolkit with reproducible recipes, self-supervised front-ends, and off-the-shelf models","date":"2024-01-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":null,"inline_ok":false,"code_sha256_prefix":"c9763c5a4bcb7e32","mcp_get_code":{"code_sha256":"c9763c5a4bcb7e32"}},{"arxiv_id":"2401.15563","paper":"/paper/brepgen-a-b-rep-generative-diffusion-model","title":"BrepGen: A B-rep Generative Diffusion Model with Structured Latent Geometry","date":"2024-01-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"samxuxiang/brepgen","path":"pc_metric.py","file_url":"https://github.com/samxuxiang/brepgen/blob/HEAD/pc_metric.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"680bb1e97d16d80c","mcp_get_code":{"code_sha256":"680bb1e97d16d80c"}},{"arxiv_id":"2307.01946","paper":"/paper/a-synthetic-electrocardiogram-ecg-image","title":"ECG-Image-Kit: A Synthetic Image Generation Toolbox to Facilitate Deep Learning-Based Electrocardiogram Digitization","date":"2023-07-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"alphanumericslab/ecg-image-kit","path":"codes/ecg-image-generator/helper_functions.py","file_url":"https://github.com/alphanumericslab/ecg-image-kit/blob/HEAD/codes/ecg-image-generator/helper_functions.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"4069a5950b04a536","mcp_get_code":{"code_sha256":"4069a5950b04a536"}},{"arxiv_id":"2306.15350","paper":"/paper/cellvit-vision-transformers-for-precise-cell","title":"CellViT: Vision Transformers for Precise Cell Segmentation and Classification","date":"2023-06-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"junlinguo/cellvit-kidney","path":"cell_segmentation/inference/inference_cellvit_experiment_kidney.py","file_url":"https://github.com/junlinguo/cellvit-kidney/blob/HEAD/cell_segmentation/inference/inference_cellvit_experiment_kidney.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"e8c211a8a925525e","mcp_get_code":{"code_sha256":"e8c211a8a925525e"}},{"arxiv_id":"2305.10601","paper":"/paper/tree-of-thoughts-deliberate-problem-solving-1","title":"Tree of Thoughts: Deliberate Problem Solving with Large Language Models","date":"2023-05-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"appl-team/appl","path":"src/appl/utils.py","file_url":"https://github.com/appl-team/appl/blob/HEAD/src/appl/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"06226fb0ae82092a","mcp_get_code":{"code_sha256":"06226fb0ae82092a"}},{"arxiv_id":"2206.13602","paper":"/paper/molecular-geometry-pretraining-with-se-3","title":"Molecular Geometry Pretraining with SE(3)-Invariant Denoising Distance Matching","date":"2022-06-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"chao1224/geossl","path":"Geom3D/datasets/PDBBind_utils.py","file_url":"https://github.com/chao1224/geossl/blob/HEAD/Geom3D/datasets/PDBBind_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3bc223bc2ae6e073","mcp_get_code":{"code_sha256":"3bc223bc2ae6e073"}},{"arxiv_id":"2206.03065","paper":"/paper/universal-speech-enhancement-with-score-based","title":"Universal Speech Enhancement with Score-based Diffusion","date":"2022-06-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"line/open-universe","path":"open_universe/bin/enhance.py","file_url":"https://github.com/line/open-universe/blob/HEAD/open_universe/bin/enhance.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"65067ffb6e848832","mcp_get_code":{"code_sha256":"65067ffb6e848832"}},{"arxiv_id":"2108.08420","paper":"/paper/d3d-hoi-dynamic-3d-human-object-interactions","title":"D3D-HOI: Dynamic 3D Human-Object Interactions from Videos","date":"2021-08-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"facebookresearch/d3d-hoi","path":"visualization/visualize_data.py","file_url":"https://github.com/facebookresearch/d3d-hoi/blob/HEAD/visualization/visualize_data.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"680bb1e97d16d80c","mcp_get_code":{"code_sha256":"680bb1e97d16d80c"}},{"arxiv_id":"2005.07143","paper":"/paper/ecapa-tdnn-emphasized-channel-attention","title":"ECAPA-TDNN: Emphasized Channel Attention, Propagation and Aggregation in TDNN Based Speaker Verification","date":null,"month_inferred_from_arxiv_id":"2020-05","title_source":"archive","repo":"gzhu06/tdspkr-mismatch-study","path":"preprocessing/wavform_extract.py","file_url":"https://github.com/gzhu06/tdspkr-mismatch-study/blob/HEAD/preprocessing/wavform_extract.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"9dbd7f6108f96407","mcp_get_code":{"code_sha256":"9dbd7f6108f96407"}},{"arxiv_id":"1912.06395","paper":"/paper/neural-cages-for-detail-preserving-3d","title":"Neural Cages for Detail-Preserving 3D Deformations","date":"2019-12-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yifita/deep_cage","path":"common.py","file_url":"https://github.com/yifita/deep_cage/blob/HEAD/common.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7041daab71192326","mcp_get_code":{"code_sha256":"7041daab71192326"}},{"arxiv_id":"1910.11480","paper":"/paper/parallel-wavegan-a-fast-waveform-generation","title":"Parallel WaveGAN: A fast waveform generation model based on generative adversarial networks with multi-resolution spectrogram","date":"2019-10-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mukeshv0/ParallelWaveGAN","path":"parallel_wavegan/utils/utils.py","file_url":"https://github.com/mukeshv0/ParallelWaveGAN/blob/HEAD/parallel_wavegan/utils/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"562a6efae0225990","mcp_get_code":{"code_sha256":"562a6efae0225990"}},{"arxiv_id":"1910.10909","paper":"/paper/espnet-tts-unified-reproducible-and","title":"ESPnet-TTS: Unified, Reproducible, and Integratable Open Source End-to-End Text-to-Speech Toolkit","date":"2019-10-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":null,"inline_ok":false,"code_sha256_prefix":"c9763c5a4bcb7e32","mcp_get_code":{"code_sha256":"c9763c5a4bcb7e32"}},{"arxiv_id":"1904.08352","paper":"/paper/mosnet-deep-learning-based-objective","title":"MOSNet: Deep Learning based Objective Assessment for Voice Conversion","date":"2019-04-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lochenchou/MOSNet","path":"custom_test.py","file_url":"https://github.com/lochenchou/MOSNet/blob/HEAD/custom_test.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"104d4b84d27c8069","mcp_get_code":{"code_sha256":"104d4b84d27c8069"}},{"arxiv_id":"1609.03499","paper":"/paper/wavenet-a-generative-model-for-raw-audio","title":"WaveNet: A Generative Model for Raw Audio","date":"2016-09-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ibab/tensorflow-wavenet","path":"wavenet/audio_reader.py","file_url":"https://github.com/ibab/tensorflow-wavenet/blob/HEAD/wavenet/audio_reader.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"605a3067a4dbc087","mcp_get_code":{"code_sha256":"605a3067a4dbc087"}}]}